I understand why “prompt kiddie” feels accurate, but I don’t think it is. Expertise is _amplified_ with LLM agents. The same $300 of tokens given to my plumber—who is an _excellent_ plumber—is unlikely to produce the same outcome.
I agree with you in that I now feel like a 100x engineer, but I think it would have taken me a long time to figure that one out pre AI.
AI is the new BASIC.
The GP poster is someone who is familiar with the problems caused by AI writing code. For example: reimplementing major functionality because the AI isn’t aware of it (somehow). There are many tasks that domain-specific knowledge is required to successfully guide the AI.
Apparently this is not one of them, though, so it’s an interesting story.
I’m also amazed at how often I can speed up the process by reviewing progress, inserting my knowledge, stopping it from pursuing dead ends, and redirecting effort. Something that the LLM might have finished in 2 hours can be done in 20 minutes with me paying close attention and intervening.
My evidence: https://ericpardee.github.io/fire-hd-ownership/blog-assets/g...
"Worked for 8h 5m"
When agents are working for 8 hours, the prompt matters a lot less. At that point you're basically just writing "Root this tablet connected via USB cable" and the prompt doesn't really matter much.
Seriously, he really didn't prompt much.
Look at what he fed into ChatGPT: https://ericpardee.github.io/fire-hd-ownership/blog-assets/c...
The only human-written part of the prompt was "Explain to me in more simple terms the following". The rest of the prompt came from asking Kimi K3 for a handoff summary.
I think part of what it shows is how much a good value function provides in a search problem. But we’ve always known that brute force is also a valid solution to search problems. It seems that what’s changed is that it’s much easier than before to run brute force, so it’s going to be applied more often and by a base rate argument “solve” more problems. What remains to be seen is whether human + new tech = better than the parts, as has been the case with most previous new technologies.
You do need a baseline of knowledge to be able to prompt the AI in a domain successfully. But beyond that baseline there are rapidly diminishing returns. Someone with skill far beyond a certain line won't get amplified the same way someone who just clears that line will.
Like, what, we are all supposed to close the book on this abstract, very new question. Something that by any approximation is extremely nuanced and also pretty meaningful? Like, "oh OK, well I guess that's that then!"
In this case he is right : https://news.ycombinator.com/item?id=49239999
The Tao reference is someone far ABOVE the line getting excellent results from the LLM.
So in this case the Tao example refutes the GGP claim.
A great X will be able to do far more great X stuff (breadth), and perhaps also be a greater X (depth).
But it's most certainly weighted in favor of the former than the latter.